2018 - 3rd International Workshop on Learning Representations for Big Networks

Our world is networked: people are closer to each other through online social network services or mobile communication networks, while information is capable to be exchanged faster by World Wide Web or email networks. The network is a treasure trove of user experiences and knowledge that presents great opportunities to understand the fundamental science of our world. On the other hand, the network, with huge amount of data, and multiple types of entities (e.g., users, documents, organizations, etc.), user behaviors, and relations between entities, has become so large and complex that traditional methodologies are inadequate.
Over the last two decades, the conventional paradigm of mining and learning with networks usually starts from the explicit exploration of their structural properties. But many of such properties, such as betweenness centrality, triangle count, and modularity, require extensive domain knowledge and expensive computation to handcrafted. In light of these issues, as well as the opportunities offered by the recent emergence of representation learning, learning latent representations for networks, a.k.a., network embedding, has recently been extensively studied in order to automatically discover and map a network's structural properties into a latent space.
This workshop aims to provide a forum for presenting the most recent advances in network representation learning to unearth rich knowledge. We expect novel research works that address various aspects and challenges of this task, including learning representation for big networks, heterogeneous network embedding, embedding dynamic networks, scalable and efficient algorithms for graph embeddings, novel platforms and applications supporting network embeddings, comparisons between structural- and embedding-based network learning techniques, and beyond.
The WWW 2018 edition of BigNet invites leading experts in the area from all over the world. Thus, it serves also as a networking event both for connecting researchers from diverse research areas such as algorithms, data mining, and machine learning, and for connecting researchers from different geographic regions.